tigr-glimmer3 - Online in the Cloud

PROGRAM:

NAME

tigr-glimmer — Find/Score potential genes in genome-file using the probability model in
icm-file

SYNOPSIS

tigr-glimmer3 [genome-file] [icm-file] [[options]]

DESCRIPTION

tigr-glimmer is a system for finding genes in microbial DNA, especially the genomes of
bacteria and archaea. tigr-glimmer (Gene Locator and Interpolated Markov Modeler) uses
interpolated Markov models (IMMs) to identify the coding regions and distinguish them from
noncoding DNA. The IMM approach, described in our Nucleic Acids Research paper on tigr-
glimmer 1.0 and in our subsequent paper on tigr-glimmer 2.0, uses a combination of Markov
models from 1st through 8th-order, weighting each model according to its predictive power.
tigr-glimmer 1.0 and 2.0 use 3-periodic nonhomogenous Markov models in their IMMs.

tigr-glimmer is the primary microbial gene finder at TIGR, and has been used to annotate
the complete genomes of B. burgdorferi (Fraser et al., Nature, Dec. 1997), T. pallidum
(Fraser et al., Science, July 1998), T. maritima, D. radiodurans, M. tuberculosis, and
non-TIGR projects including C. trachomatis, C. pneumoniae, and others. Its analyses of
some of these genomes and others is available at the TIGR microbial database site.

A special version of tigr-glimmer designed for small eukaryotes, GlimmerM, was used to
find the genes in chromosome 2 of the malaria parasite, P. falciparum.. GlimmerM is
described in S.L. Salzberg, M. Pertea, A.L. Delcher, M.J. Gardner, and H. Tettelin,
"Interpolated Markov models for eukaryotic gene finding," Genomics 59 (1999), 24-31.
Click here (http://www.tigr.org/software/glimmerm/) to visit the GlimmerM site, which
includes information on how to download the GlimmerM system.

The tigr-glimmer system consists of two main programs. The first of these is the training
program, build-imm. This program takes an input set of sequences and builds and outputs
the IMM for them. These sequences can be complete genes or just partial orfs. For a new
genome, this training data can consist of those genes with strong database hits as well as
very long open reading frames that are statistically almost certain to be genes. The
second program is glimmer, which uses this IMM to identify putative genes in an entire
genome. tigr-glimmer automatically resolves conflicts between most overlapping genes by
choosing one of them. It also identifies genes that are suspected to truly overlap, and
flags these for closer inspection by the user. These ``suspect'' gene candidates have been
a very small percentage of the total for all the genomes analyzed thus far. tigr-glimmer
is a program that...

OPTIONS

-C n Use n as GC percentage of independent model

Note: n should be a percentage, e.g., -C 45.2

-f Use ribosome-binding energy to choose start codon

+f Use first codon in orf as start codon

-g n Set minimum gene length to n

-i filename
Use filename to select regions of bases that are off limits, so that no bases
within that area will be examined

-l Assume linear rather than circular genome, i.e., no wraparound

-L filename
Use filename to specify a list of orfs that should be scored separately, with no
overlap rules

-M Input is a multifasta file of separate genes to be scored separately, with no
overlap rules

-o n Set minimum overlap length to n. Overlaps shorter than this are ignored.

-p n Set minimum overlap percentage to n%. Overlaps shorter than this percentage of
*both* strings are ignored.

-q n Set the maximum length orf that can be rejected because of the independent
probability score column to (n - 1)

-r Don't use independent probability score column

+r Use independent probability score column

-r Don't use independent probability score column

-s s Use string s as the ribosome binding pattern to find start codons.

+S Do use stricter independent intergenic model that doesn't give probabilities to
in-frame stop codons. (Option is obsolete since this is now the only behaviour

-t n Set threshold score for calling as gene to n. If the in-frame score >= n, then
the region is given a number and considered a potential gene.

-w n Use "weak" scores on tentative genes n or longer. Weak scores ignore the
independent probability score.

Use tigr-glimmer3 online using onworks.net services